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Spearman Correlation of Models

Summary of 5_Default_NeuralNetwork
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Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
1.8 seconds
Metric details
|
score |
threshold |
| logloss |
0.76868 |
nan |
| auc |
0.563967 |
nan |
| f1 |
0.651564 |
0.00190441 |
| accuracy |
0.5576 |
0.426766 |
| precision |
0.59322 |
0.888019 |
| recall |
1 |
0.00190441 |
| mcc |
0.112859 |
0.426766 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.76868 |
nan |
| auc |
0.563967 |
nan |
| f1 |
0.530161 |
0.426766 |
| accuracy |
0.5576 |
0.426766 |
| precision |
0.544503 |
0.426766 |
| recall |
0.516556 |
0.426766 |
| mcc |
0.112859 |
0.426766 |
Confusion matrix (at threshold=0.426766)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
385 |
261 |
| Labeled as 1 |
292 |
312 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 1_Baseline
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Baseline Classifier (Baseline)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
0.2 seconds
Metric details
|
score |
threshold |
| logloss |
0.692583 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.651564 |
0.43488 |
| accuracy |
0.4832 |
0.43488 |
| precision |
0.4832 |
0.43488 |
| recall |
1 |
0.43488 |
| mcc |
0 |
0.43488 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.692583 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.651564 |
0.43488 |
| accuracy |
0.4832 |
0.43488 |
| precision |
0.4832 |
0.43488 |
| recall |
1 |
0.43488 |
| mcc |
0 |
0.43488 |
Confusion matrix (at threshold=0.43488)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
0 |
646 |
| Labeled as 1 |
0 |
604 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of Ensemble
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Ensemble structure
Metric details
|
score |
threshold |
| logloss |
0.682999 |
nan |
| auc |
0.603392 |
nan |
| f1 |
0.661972 |
0.316161 |
| accuracy |
0.5776 |
0.451899 |
| precision |
0.613333 |
0.721944 |
| recall |
1 |
0.101465 |
| mcc |
0.194736 |
0.381235 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.682999 |
nan |
| auc |
0.603392 |
nan |
| f1 |
0.609467 |
0.451899 |
| accuracy |
0.5776 |
0.451899 |
| precision |
0.550802 |
0.451899 |
| recall |
0.682119 |
0.451899 |
| mcc |
0.165133 |
0.451899 |
Confusion matrix (at threshold=0.451899)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
310 |
336 |
| Labeled as 1 |
192 |
412 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 2_DecisionTree
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Decision Tree
- n_jobs: -1
- criterion: gini
- max_depth: 3
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
5.7 seconds
Metric details
|
score |
threshold |
| logloss |
0.700574 |
nan |
| auc |
0.517821 |
nan |
| f1 |
0.651564 |
0.366983 |
| accuracy |
0.5288 |
0.500811 |
| precision |
0.51928 |
0.500811 |
| recall |
1 |
0.366983 |
| mcc |
0.0485308 |
0.500811 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.700574 |
nan |
| auc |
0.517821 |
nan |
| f1 |
0.406848 |
0.500811 |
| accuracy |
0.5288 |
0.500811 |
| precision |
0.51928 |
0.500811 |
| recall |
0.334437 |
0.500811 |
| mcc |
0.0485308 |
0.500811 |
Confusion matrix (at threshold=0.500811)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
459 |
187 |
| Labeled as 1 |
402 |
202 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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Summary of 6_Default_RandomForest
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Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
6.3 seconds
Metric details
|
score |
threshold |
| logloss |
0.689063 |
nan |
| auc |
0.558326 |
nan |
| f1 |
0.656683 |
0.414255 |
| accuracy |
0.5552 |
0.490009 |
| precision |
0.549793 |
0.490009 |
| recall |
1 |
0.3158 |
| mcc |
0.12066 |
0.46638 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.689063 |
nan |
| auc |
0.558326 |
nan |
| f1 |
0.488029 |
0.490009 |
| accuracy |
0.5552 |
0.490009 |
| precision |
0.549793 |
0.490009 |
| recall |
0.438742 |
0.490009 |
| mcc |
0.105571 |
0.490009 |
Confusion matrix (at threshold=0.490009)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
429 |
217 |
| Labeled as 1 |
339 |
265 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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Summary of 4_Default_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
25.3 seconds
Metric details
|
score |
threshold |
| logloss |
0.698165 |
nan |
| auc |
0.551788 |
nan |
| f1 |
0.651564 |
0.166114 |
| accuracy |
0.5472 |
0.438415 |
| precision |
0.537415 |
0.520788 |
| recall |
1 |
0.166114 |
| mcc |
0.110456 |
0.438415 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.698165 |
nan |
| auc |
0.551788 |
nan |
| f1 |
0.602528 |
0.438415 |
| accuracy |
0.5472 |
0.438415 |
| precision |
0.523171 |
0.438415 |
| recall |
0.710265 |
0.438415 |
| mcc |
0.110456 |
0.438415 |
Confusion matrix (at threshold=0.438415)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
255 |
391 |
| Labeled as 1 |
175 |
429 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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Summary of 3_Linear
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Logistic Regression (Linear)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
2.2 seconds
Metric details
|
score |
threshold |
| logloss |
0.682999 |
nan |
| auc |
0.603392 |
nan |
| f1 |
0.661972 |
0.316161 |
| accuracy |
0.5776 |
0.451899 |
| precision |
0.613333 |
0.721944 |
| recall |
1 |
0.101465 |
| mcc |
0.194736 |
0.381235 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.682999 |
nan |
| auc |
0.603392 |
nan |
| f1 |
0.609467 |
0.451899 |
| accuracy |
0.5776 |
0.451899 |
| precision |
0.550802 |
0.451899 |
| recall |
0.682119 |
0.451899 |
| mcc |
0.165133 |
0.451899 |
Confusion matrix (at threshold=0.451899)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
310 |
336 |
| Labeled as 1 |
192 |
412 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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